Analysis of Vegetation Infection Information Using Unmanned Aerial Vehicle with Optical Sensor

Jung, Kyu-In; Park, Joon Kyu

doi:10.18494/sam.2019.2465

Cited by 11 publications

(2 citation statements)

References 2 publications

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“…Some of them are based on aerial images taken at a height of hundreds of meters, and they are very different from those that we process, which are taken at only some meters from the ground. Therefore, those images include wide areas with many trees or plants, and solutions are proposed for the detection of dead, infected, or withered pines [54][55][56] or the detection of plant failures in rows of planted crops [57,58]. Other articles [43,[59][60][61] are based on aerial images taken at a low height from the ground and are more comparable to ours.…”

Section: Discussionmentioning

confidence: 85%

Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone

Martinez-Heredia

Gálvez

Colodro

et al. 2023

Drones

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Drones can be very helpful in precision agriculture. Currently, most drone-based solutions for plant disease detection incorporate multispectral, hyperspectral, or thermal cameras, which are expensive. In addition, there is a trend nowadays to apply machine learning techniques to precision agriculture, which are computationally complex and intensive. In this work, we explore the feasibility of detecting ochre spot disease in almond plantations based on conventional techniques of computer vision and images from a very low-cost RGB camera that is placed on board a drone. Such an approach will allow the detection system to be simple and inexpensive. First, we made a study of color on the ochre spot disease. Second, we developed a specific algorithm that was capable of processing and analyzing limited-quality images from a very low-cost camera. In addition, it can estimate the percentage of healthy and unhealthy parts of the plant. Thanks to the GPS on board the drone, the system can provide the location of every sick almond tree. Third, we checked the operation of the algorithm with a variety of photographs of ochre spot disease in almonds. The study demonstrates that the efficiency of the algorithm depends to a great extent on environmental conditions, but, despite the limitations, the results obtained with the analyzed photographs show a maximum discrepancy of 10% between the estimated percentage and the ground truth percentage of the unhealthy area. This approach shows great potential for extension to other crops by making previous studies of color and adaptations.

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Section: Discussionmentioning

confidence: 85%

Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone

Martinez-Heredia

Gálvez

Colodro

et al. 2023

Drones

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show abstract

“…Intellectualization aspect. RGB images acquired by UAVs with optical sensors are used to detect the plants damaged by diseases and pests and provide data for forest management [40]. The model based on the RGB images is applied for the recognition of plant leaves to analyze the possibility of pest attack and automatically detects the stage of the pest attack [41].…”

Section: Application Potential Of Machine Learning Algorithm In Yellow Leaf Disease Of Arecanut Monitoringmentioning

confidence: 99%

Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

et al. 2021

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Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.

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Drift reduction in orchards through the use of an autonomous UAV system

Sánchez-Fernández

Barrera

Martínez-Guanter³

et al. 2023

Computers and Electronics in Agriculture

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Analysis of Vegetation Infection Information Using Unmanned Aerial Vehicle with Optical Sensor

Cited by 11 publications

References 2 publications

Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone

Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone

Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

Drift reduction in orchards through the use of an autonomous UAV system

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